{"id":"W2010689979","doi":"10.1016/j.jmva.2010.01.013","title":"Generating random AR(<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si60.gif\" display=\"inline\" overflow=\"scroll\"><mml:mi>p</mml:mi></mml:math>) and MA(<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si61.gif\" display=\"inline\" overflow=\"scroll\"><mml:mi>q</mml:mi></mml:math>) Toeplitz correlation matrices","year":2010,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Toeplitz matrix; Mathematics; Autoregressive model; Invertible matrix; Series (stratigraphy); Gaussian; Matrix (chemical analysis); Discrete mathematics; Applied mathematics; Combinatorics; Pure mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.005057315,0.001305402,0.001361669,0.001050938,0.001970577,0.002512791,0.002365124,0.001750425,0.0001033753],"category_scores_gemma":[0.001594798,0.001417719,0.002615762,0.001956425,0.000593337,0.003044778,0.001531699,0.002650672,0.000399062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007284856,"about_ca_system_score_gemma":0.001024249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00160011,"about_ca_topic_score_gemma":0.0008003386,"domain_scores_codex":[0.9894722,0.0007043875,0.003281974,0.002006924,0.002677968,0.001856521],"domain_scores_gemma":[0.9894304,0.002054126,0.004433414,0.002461351,0.0005192429,0.001101441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001068614,0.0005254315,0.0000995735,0.0003503771,0.002918098,0.0007906901,0.002260237,0.01737121,0.01138629,0.9552499,0.0004329855,0.007546569],"study_design_scores_gemma":[0.003875915,0.0006857151,0.0005008447,0.0005531282,0.003481307,0.001034577,0.0003421996,0.9810415,0.004518429,0.001108703,0.001631771,0.001225919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7498211,0.001004404,0.2456795,0.0005830097,0.001907401,0.0001325765,0.0001399339,0.0001912874,0.0005407821],"genre_scores_gemma":[0.8501847,0.001054501,0.1446145,0.0009813517,0.002239977,0.0001681434,0.0002481193,0.0003039376,0.0002047581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9636703,"threshold_uncertainty_score":0.9999698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01476107882036761,"score_gpt":0.2582122102370381,"score_spread":0.2434511314166705,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}